1

Import the ESRI shapefile of German districts and the district attribute table. Join the two data frames, transform the CRS to EPSG:3035 and check your changes.

You need to rename one of the id variables or adjust your join accordingly (AGS = district_id).

solution 

# load libraries
library(sf)
library(dplyr)

# Import data
german_districts <- 
  sf::read_sf("./data/VG250_KRS.shp") %>% 
  dplyr::rename(district_id = AGS)

attributes_districts <- readr::read_csv("./data/attributes_districts.csv") 
## Rows: 411 Columns: 5
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): district_id
## dbl (4): population, death_rate, death7_lk, afd_voteshare_2021
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Join data and transform
german_districts_enhanced <- 
  german_districts %>% 
  dplyr::left_join(attributes_districts, by = "district_id") %>% 
  sf::st_transform(3035)

# Check
sf::st_crs(german_districts_enhanced)
## Coordinate Reference System:
##   User input: EPSG:3035 
##   wkt:
## PROJCRS["ETRS89-extended / LAEA Europe",
##     BASEGEOGCRS["ETRS89",
##         DATUM["European Terrestrial Reference System 1989",
##             ELLIPSOID["GRS 1980",6378137,298.257222101,
##                 LENGTHUNIT["metre",1]]],
##         PRIMEM["Greenwich",0,
##             ANGLEUNIT["degree",0.0174532925199433]],
##         ID["EPSG",4258]],
##     CONVERSION["Europe Equal Area 2001",
##         METHOD["Lambert Azimuthal Equal Area",
##             ID["EPSG",9820]],
##         PARAMETER["Latitude of natural origin",52,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8801]],
##         PARAMETER["Longitude of natural origin",10,
##             ANGLEUNIT["degree",0.0174532925199433],
##             ID["EPSG",8802]],
##         PARAMETER["False easting",4321000,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8806]],
##         PARAMETER["False northing",3210000,
##             LENGTHUNIT["metre",1],
##             ID["EPSG",8807]]],
##     CS[Cartesian,2],
##         AXIS["northing (Y)",north,
##             ORDER[1],
##             LENGTHUNIT["metre",1]],
##         AXIS["easting (X)",east,
##             ORDER[2],
##             LENGTHUNIT["metre",1]],
##     USAGE[
##         SCOPE["Statistical analysis."],
##         AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Turkey; United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],
##         BBOX[24.6,-35.58,84.17,44.83]],
##     ID["EPSG",3035]]
head(german_districts_enhanced, 2)
## Simple feature collection with 2 features and 27 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4279627 ymin: 3460480 xmax: 4335232 ymax: 3524426
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 2 × 28
##     ADE    GF   BSG ARS   district_id SDV_ARS    GEN   BEZ     IBZ BEM   NBD   SN_L  SN_R  SN_K  SN_V1 SN_V2 SN_G 
##   <int> <int> <int> <chr> <chr>       <chr>      <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1     4     4     1 01001 01001       010010000… Flen… Krei…    40 --    ja    01    0     01    00    00    000  
## 2     4     4     1 01002 01002       010020000… Kiel  Krei…    40 --    ja    01    0     02    00    00    000  
## # … with 11 more variables: FK_S3 <chr>, NUTS <chr>, ARS_0 <chr>, AGS_0 <chr>, WSK <date>, DEBKG_ID <chr>,
## #   geometry <MULTIPOLYGON [m]>, population <dbl>, death_rate <dbl>, death7_lk <dbl>, afd_voteshare_2021 <dbl>

2

We want a first descriptive visual of the distribution of Covid-19 deaths in Konstanz and the surrounding districts. Calculate the number of Covid-19 deaths (death_rate) by population (population) and multiply with 100,000.

Filter Konstanz (district_id == "08335"), find the surrounding districts, and plot Konstanz and its surrounding districts.

You can use the dplyr function sf::bind_rows() to combine the two spatial objects, “Konstanz” and “Konstanz Surroundings”.

solution 

# calculate Covid-19 rate
german_districts_enhanced <-
  german_districts_enhanced %>% 
  dplyr::mutate(covid_deaths_pop = (death_rate / population) * 100000)

# filter Cologne
konstanz <-
  german_districts_enhanced %>% 
  dplyr::filter(district_id == "08335")

# filter surrounding districts, append with Cologne data and select the Covid column
konstanz_sur <-
  german_districts_enhanced %>%
  dplyr::filter(lengths(sf::st_touches(., konstanz)) > 0) %>% 
  dplyr::bind_rows(konstanz) %>%   
  dplyr::select(covid_deaths_pop)

# plot  
plot(konstanz_sur)

3

Save your data set of Konstanz and its surrounding districts as an ESRI Shapefile.

solution 

# Export as shapefile
sf::st_write(
  cologne_sur, 
  dsn = "./data/participant_material/cologne_covid19_epsg3035.shp", 
  delete_layer = TRUE
)